{
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  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": 3
  },
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  "kernelspec": {
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   "display_name": "Python 3.6.4 64-bit ('base': conda)"
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]  # 用来正常显示中文标签\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False  # 用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                  股票A         股票B\ndate                             \n2012-1-1    76.985291   40.301222\n2012-2-1    71.506866   33.981832\n2012-3-1    57.410031   51.475666\n2012-4-1    56.485801   41.560597\n2012-5-1    39.225449   27.668570\n...               ...         ...\n2021-10-1  141.698789  199.537386\n2021-11-1  149.804607  215.963365\n2021-12-1  143.574939  223.464030\n2022-1-1   117.443904  196.151518\n2022-2-1   144.976472  217.230120\n\n[122 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>股票A</th>\n      <th>股票B</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-1-1</th>\n      <td>76.985291</td>\n      <td>40.301222</td>\n    </tr>\n    <tr>\n      <th>2012-2-1</th>\n      <td>71.506866</td>\n      <td>33.981832</td>\n    </tr>\n    <tr>\n      <th>2012-3-1</th>\n      <td>57.410031</td>\n      <td>51.475666</td>\n    </tr>\n    <tr>\n      <th>2012-4-1</th>\n      <td>56.485801</td>\n      <td>41.560597</td>\n    </tr>\n    <tr>\n      <th>2012-5-1</th>\n      <td>39.225449</td>\n      <td>27.668570</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2021-10-1</th>\n      <td>141.698789</td>\n      <td>199.537386</td>\n    </tr>\n    <tr>\n      <th>2021-11-1</th>\n      <td>149.804607</td>\n      <td>215.963365</td>\n    </tr>\n    <tr>\n      <th>2021-12-1</th>\n      <td>143.574939</td>\n      <td>223.464030</td>\n    </tr>\n    <tr>\n      <th>2022-1-1</th>\n      <td>117.443904</td>\n      <td>196.151518</td>\n    </tr>\n    <tr>\n      <th>2022-2-1</th>\n      <td>144.976472</td>\n      <td>217.230120</td>\n    </tr>\n  </tbody>\n</table>\n<p>122 rows × 2 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "df = pd.read_csv(\"TimeSeriesData.csv\", index_col=0, encoding='gbk')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['2012-1-1', '2012-2-1', '2012-3-1', '2012-4-1', '2012-5-1', '2012-6-1',\n       '2012-7-1', '2012-8-1', '2012-9-1', '2012-10-1',\n       ...\n       '2021-5-1', '2021-6-1', '2021-7-1', '2021-8-1', '2021-9-1', '2021-10-1',\n       '2021-11-1', '2021-12-1', '2022-1-1', '2022-2-1'],\n      dtype='object', name='date', length=122)"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<class 'str'>\n"
    }
   ],
   "source": [
    "print(type(df.index[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "股票A 股票B\n"
    }
   ],
   "source": [
    "data_1,data_2 = df.columns[0],df.columns[1]\n",
    "print(data_1,data_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.index\n",
    "y1 = df[data_1]\n",
    "y2 = df[data_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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x=\"350\" xlink:href=\"#SimHei-49\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_22\">\r\n     <g id=\"line2d_22\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"97.241623\" xlink:href=\"#m0ead44599e\" y=\"224.64\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_22\">\r\n      <!-- 2013-10-1 -->\r\n      <g transform=\"translate(74.741623 238.515)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#SimHei-50\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-48\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-49\"/>\r\n       <use x=\"150\" xlink:href=\"#SimHei-51\"/>\r\n       <use x=\"200\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"250\" xlink:href=\"#SimHei-49\"/>\r\n       <use x=\"300\" xlink:href=\"#SimHei-48\"/>\r\n       <use x=\"350\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"400\" xlink:href=\"#SimHei-49\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_23\">\r\n     <g id=\"line2d_23\">\r\n      <g>\r\n       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id=\"text_24\">\r\n      <!-- 2013-12-1 -->\r\n      <g transform=\"translate(79.772427 238.515)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#SimHei-50\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-48\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-49\"/>\r\n       <use x=\"150\" xlink:href=\"#SimHei-51\"/>\r\n       <use x=\"200\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"250\" xlink:href=\"#SimHei-49\"/>\r\n       <use x=\"300\" xlink:href=\"#SimHei-50\"/>\r\n       <use x=\"350\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"400\" xlink:href=\"#SimHei-49\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_25\">\r\n     <g id=\"line2d_25\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"104.787829\" xlink:href=\"#m0ead44599e\" y=\"224.64\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_25\">\r\n      <!-- 2014-1-1 -->\r\n      <g transform=\"translate(84.787829 238.515)scale(0.1 -0.1)\">\r\n       <use 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x=\"200\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"250\" xlink:href=\"#SimHei-50\"/>\r\n       <use x=\"300\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"350\" xlink:href=\"#SimHei-49\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_27\">\r\n     <g id=\"line2d_27\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"109.818633\" xlink:href=\"#m0ead44599e\" y=\"224.64\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_27\">\r\n      <!-- 2014-3-1 -->\r\n      <g transform=\"translate(89.818633 238.515)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#SimHei-50\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-48\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-49\"/>\r\n       <use x=\"150\" xlink:href=\"#SimHei-52\"/>\r\n       <use x=\"200\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"250\" xlink:href=\"#SimHei-51\"/>\r\n       <use x=\"300\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"350\" xlink:href=\"#SimHei-49\"/>\r\n      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y=\"224.64\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_29\">\r\n      <!-- 2014-5-1 -->\r\n      <g transform=\"translate(94.849437 238.515)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#SimHei-50\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-48\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-49\"/>\r\n       <use x=\"150\" xlink:href=\"#SimHei-52\"/>\r\n       <use x=\"200\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"250\" xlink:href=\"#SimHei-53\"/>\r\n       <use x=\"300\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"350\" xlink:href=\"#SimHei-49\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_30\">\r\n     <g id=\"line2d_30\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"117.364838\" xlink:href=\"#m0ead44599e\" y=\"224.64\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_30\">\r\n      <!-- 2014-6-1 -->\r\n      <g transform=\"translate(97.364838 238.515)scale(0.1 -0.1)\">\r\n       <use 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xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"250\" xlink:href=\"#SimHei-55\"/>\r\n       <use x=\"300\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"350\" xlink:href=\"#SimHei-49\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_32\">\r\n     <g id=\"line2d_32\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"122.395642\" xlink:href=\"#m0ead44599e\" y=\"224.64\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_32\">\r\n      <!-- 2014-8-1 -->\r\n      <g transform=\"translate(102.395642 238.515)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#SimHei-50\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-48\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-49\"/>\r\n       <use x=\"150\" xlink:href=\"#SimHei-52\"/>\r\n       <use x=\"200\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"250\" xlink:href=\"#SimHei-56\"/>\r\n       <use x=\"300\" xlink:href=\"#SimHei-45\"/>\r\n       <use x=\"350\" xlink:href=\"#SimHei-49\"/>\r\n      </g>\r\n     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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 直接画图\n",
    "fig = plt.figure()\n",
    "plt.plot(x, y1)\n",
    "plt.plot(x, y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "'2012-1-1'"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "df.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "datetime.date(2012, 1, 1)"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "df.index = [datetime.strptime(d, \"%Y-%m-%d\").date() for d in df.index]\n",
    "df.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.index\n",
    "y1 = df[data_1]\n",
    "y2 = df[data_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 折线图\n",
    "fig = plt.figure()\n",
    "plt.plot(x, y1)\n",
    "plt.plot(x, y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 600x400 with 1 Axes>",
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52.34375 33 57.421875 \r\nQ 30.078125 62.5 25.1875 62.6875 \r\nQ 20.3125 62.890625 16.59375 57.8125 \r\nQ 12.890625 52.734375 10.546875 31.25 \r\nz\r\n\" id=\"SimHei-48\"/>\r\n       <path d=\"M 21.875 56.25 \r\nQ 16.796875 51.171875 8.984375 46.484375 \r\nL 8.984375 53.90625 \r\nQ 18.75 60.546875 25 69.53125 \r\nL 29.6875 69.53125 \r\nL 29.6875 2.34375 \r\nL 21.875 2.34375 \r\nz\r\n\" id=\"SimHei-49\"/>\r\n      </defs>\r\n      <g transform=\"translate(46.543182 253.89)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#SimHei-50\"/>\r\n       <use x=\"50\" xlink:href=\"#SimHei-48\"/>\r\n       <use x=\"100\" xlink:href=\"#SimHei-49\"/>\r\n       <use x=\"150\" xlink:href=\"#SimHei-50\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_2\">\r\n     <g id=\"line2d_2\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"116.936726\" xlink:href=\"#mfa401e74e5\" y=\"240.015\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_2\">\r\n      <!-- 2014 -->\r\n 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33.59375 67.96875 37.109375 65.421875 \r\nQ 40.625 62.890625 42.1875 58.203125 \r\nQ 43.75 53.515625 41.984375 47.265625 \r\nQ 40.234375 41.015625 32.8125 37.5 \r\nQ 39.453125 35.546875 42.1875 31.4375 \r\nQ 44.921875 27.34375 44.921875 21.484375 \r\nQ 44.921875 15.625 43.15625 12.109375 \r\nQ 41.40625 8.59375 39.25 6.25 \r\nQ 37.109375 3.90625 33.390625 2.53125 \r\nQ 29.6875 1.171875 24.015625 1.171875 \r\nQ 18.359375 1.171875 14.25 2.53125 \r\nQ 10.15625 3.90625 7.421875 6.640625 \r\nQ 4.6875 9.375 3.703125 13.078125 \r\nQ 2.734375 16.796875 2.734375 26.171875 \r\nz\r\nM 10.9375 26.5625 \r\nQ 10.546875 17.1875 12.296875 13.671875 \r\nQ 14.0625 10.15625 18.75 9.171875 \r\nQ 23.4375 8.203125 28.515625 9.375 \r\nQ 33.59375 10.546875 35.546875 14.84375 \r\nQ 37.5 19.140625 36.90625 23.4375 \r\nQ 36.328125 27.734375 32.03125 30.65625 \r\nQ 27.734375 33.59375 22.65625 33.203125 \r\nQ 17.578125 32.8125 14.25 29.6875 \r\nQ 10.9375 26.5625 10.546875 17.1875 \r\nz\r\nM 12.109375 56.25 \r\nQ 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 丰富细节\n",
    "fig = plt.figure(dpi=100)\n",
    "plt.plot(x, y1, color=\"#F94306\", label=data_1)\n",
    "plt.plot(x, y2, color=\"#06BCF9\", label=data_2)\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.title(f\"{data_1} vs. {data_2}\")\n",
    "plt.legend(loc=\"upper left\", edgecolor=\"none\", facecolor=\"none\")\n",
    "\n",
    "plt.show()\n",
    "# plt.savefig(\"折线图.png\")\n",
    "# plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 面积图\n",
    "fig = plt.figure(figsize=(10, 8), dpi=350)\n",
    "colors = [\"#F94306\", \"#06BCF9\"]\n",
    "plt.fill_between(\n",
    "    df.index.values,\n",
    "    y1=df[data_1].values,\n",
    "    y2=0,\n",
    "    label=df.columns[1],\n",
    "    alpha=0.6,\n",
    "    facecolor=colors[0],\n",
    "    linewidth=1,\n",
    "    edgecolor=\"k\",\n",
    ")\n",
    "plt.fill_between(\n",
    "    df.index.values,\n",
    "    y1=df[data_2].values,\n",
    "    y2=0,\n",
    "    label=df.columns[0],\n",
    "    alpha=0.6,\n",
    "    facecolor=colors[1],\n",
    "    linewidth=1,\n",
    "    edgecolor=\"k\",\n",
    ")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.title(f\"{data_1} vs. {data_2}\")\n",
    "plt.legend(loc=\"upper left\", edgecolor=\"none\", facecolor=\"none\")\n",
    "# plt.show()\n",
    "plt.savefig(\"面积图.png\")\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                   股票A         股票B        ymin        ymax\n2012-01-01   76.985291   40.301222   40.301222   76.985291\n2012-02-01   71.506866   33.981832   33.981832   71.506866\n2012-03-01   57.410031   51.475666   51.475666   57.410031\n2012-04-01   56.485801   41.560597   41.560597   56.485801\n2012-05-01   39.225449   27.668570   27.668570   39.225449\n...                ...         ...         ...         ...\n2021-10-01  141.698789  199.537386  141.698789  199.537386\n2021-11-01  149.804607  215.963365  149.804607  215.963365\n2021-12-01  143.574939  223.464030  143.574939  223.464030\n2022-01-01  117.443904  196.151518  117.443904  196.151518\n2022-02-01  144.976472  217.230120  144.976472  217.230120\n\n[122 rows x 4 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>股票A</th>\n      <th>股票B</th>\n      <th>ymin</th>\n      <th>ymax</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-01</th>\n      <td>76.985291</td>\n      <td>40.301222</td>\n      <td>40.301222</td>\n      <td>76.985291</td>\n    </tr>\n    <tr>\n      <th>2012-02-01</th>\n      <td>71.506866</td>\n      <td>33.981832</td>\n      <td>33.981832</td>\n      <td>71.506866</td>\n    </tr>\n    <tr>\n      <th>2012-03-01</th>\n      <td>57.410031</td>\n      <td>51.475666</td>\n      <td>51.475666</td>\n      <td>57.410031</td>\n    </tr>\n    <tr>\n      <th>2012-04-01</th>\n      <td>56.485801</td>\n      <td>41.560597</td>\n      <td>41.560597</td>\n      <td>56.485801</td>\n    </tr>\n    <tr>\n      <th>2012-05-01</th>\n      <td>39.225449</td>\n      <td>27.668570</td>\n      <td>27.668570</td>\n      <td>39.225449</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2021-10-01</th>\n      <td>141.698789</td>\n      <td>199.537386</td>\n      <td>141.698789</td>\n      <td>199.537386</td>\n    </tr>\n    <tr>\n      <th>2021-11-01</th>\n      <td>149.804607</td>\n      <td>215.963365</td>\n      <td>149.804607</td>\n      <td>215.963365</td>\n    </tr>\n    <tr>\n      <th>2021-12-01</th>\n      <td>143.574939</td>\n      <td>223.464030</td>\n      <td>143.574939</td>\n      <td>223.464030</td>\n    </tr>\n    <tr>\n      <th>2022-01-01</th>\n      <td>117.443904</td>\n      <td>196.151518</td>\n      <td>117.443904</td>\n      <td>196.151518</td>\n    </tr>\n    <tr>\n      <th>2022-02-01</th>\n      <td>144.976472</td>\n      <td>217.230120</td>\n      <td>144.976472</td>\n      <td>217.230120</td>\n    </tr>\n  </tbody>\n</table>\n<p>122 rows × 4 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "# 夹层填充面积图\n",
    "df[\"ymin\"] = df[[data_1, data_2]].apply(lambda x: x.min(), axis=1)\n",
    "df[\"ymax\"] = df[[data_1, data_2]].apply(lambda x: x.max(), axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(10, 8), dpi=350)\n",
    "plt.fill_between(\n",
    "    df.index.values,\n",
    "    y1=df.ymax.values,\n",
    "    y2=df.ymin.values,\n",
    "    alpha=0.15,\n",
    "    facecolor=\"black\",\n",
    "    linewidth=1,\n",
    "    edgecolor=\"k\",\n",
    ")\n",
    "plt.plot(df.index, df[data_1], color=\"#F94306\", label=data_1)\n",
    "plt.plot(df.index, df[data_2], color=\"#06BCF9\", label=data_2)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.title(f\"{data_1} vs. {data_2}\")\n",
    "plt.legend(loc=\"upper left\", edgecolor=\"none\", facecolor=\"none\")\n",
    "\n",
    "# plt.show()\n",
    "plt.savefig(\"夹层填充面积图.png\")\n",
    "plt.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}